Institut für Wirtschaftsinformatik

Permanent URI for this collectionhttps://hohpublica.uni-hohenheim.de/handle/123456789/17752

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    Digital facilitation of group work to gain predictable performance
    (2024) Gimpel, Henner; Lahmer, Stefanie; Wöhl, Moritz; Graf-Drasch, Valerie; Gimpel, Henner; University of Hohenheim, Schloß Hohenheim 1, Stuttgart, Germany; Lahmer, Stefanie; Branch Business and Information Systems Engineering, Fraunhofer Institute for Applied Information Technology FIT, Augsburg, Germany; Wöhl, Moritz; Branch Business and Information Systems Engineering, Fraunhofer Institute for Applied Information Technology FIT, Augsburg, Germany; Graf-Drasch, Valerie; University of Hohenheim, Schloß Hohenheim 1, Stuttgart, Germany
    Group work is a commonly used method of working, and the performance of a group can vary depending on the type and structure of the task at hand. Research suggests that groups can exhibit "collective intelligence"—the ability to perform well across tasks—under certain conditions, making group performance somewhat predictable. However, predictability of task performance becomes difficult when a task relies heavily on coordination among group members or is ill-defined. To address this issue, we propose a technical solution in the form of a chatbot providing advice to facilitate group work for more predictable performance. Specifically, we target well-defined, high-coordination tasks. Through experiments with 64 virtual groups performing various tasks and communicating via text-based chat, we found a relationship between the average intelligence of group members and their group performance in such tasks, making performance more predictable. The practical implications of this research are significant, as the assembly of consistently performing groups is an important organizational activity.
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    The impact of information load on predicting success in electronic negotiations
    (2025) Kaya, Muhammed-Fatih; Schoop, Mareike; Kaya, Muhammed-Fatih; Intelligent Information Systems, Institute of Information Systems, University of Hohenheim, Schwerzstr. 40, Osthof-Nord, 70599, Stuttgart, Germany; Schoop, Mareike; Intelligent Information Systems, Institute of Information Systems, University of Hohenheim, Schwerzstr. 40, Osthof-Nord, 70599, Stuttgart, Germany
    The exchange of information is an essential means for being able to conduct negotiations and to derive situational decisions. In electronic negotiations, information is transferred in the form of requests, offers, questions and clarifications consisting of communication and decisions. Taken together, such information makes or breaks the negotiation. Whilst information analysis has traditionally been conducted through human coding, machine learning techniques now enable automated analyses. One of the grand challenges of electronic negotiation research is the generation of predictions as to whether ongoing negotiations will success or fail at the end of the negotiation process by considering the previous negotiation course. With this goal in mind, the present research paper investigates the impact of information load on predicting success and failure in electronic negotiations and how predictive machine learning models react to the successive increase of negotiation data. Information in different data combinations is used for the evaluation of various classification techniques to simulate the progress in negotiation processes and to investigate the impact of increasing information load hidden in the utility and communication data. It will be shown that the more information the merrier the result does not always hold. Instead, data-driven ML model recommendations are presented as to when and based on which data density certain models should or should not be used for the prediction of success and failure of electronic negotiations.
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    Predicting tilling and seeding operation times in grain production: a comparison of machine learning and mechanistic models
    (2025) Scheurer, Luca; Zimpel, Tobias; Leukel, Jörg
    Field operations management in grain production requires accurate and timely predictions of operation times for machine tasks. While machine learning (ML) is being adopted more widely in operations management, little is known about its ability to predict tilling and seeding operation times. The aim of this study was to evaluate the prediction performance of ML models for these operation times by using readily available tractor and operations data rather than dynamic environmental data. We collected data between March 2022 and August 2023 from 70 grain fields in the southwest of Germany, including variables such as tractor speed, engine speed, fuel consumption, and field geometry. Operation times exhibited high variability (coefficient of variation [CV] = 0.88). Nine ML algorithms and two conventional mechanistic models proposed by the American Society of Agricultural and Biological Engineers (ASAE EP496.3) were evaluated in a temporal external validation. Random forest (RF) models outperformed all other models, achieving a normalized root mean square error (NRMSE) of 0.215 and a coefficient of determination (R2) of 0.910. Compared to a conventional mechanistic model, the RF model reduced the mean absolute error (MAE) by 37.8 %, and enhanced the R2 by 0.107. The study results highlight the potential of our approach to predict tilling and seeding operation times in grain production without increasing the effort for data collection, offering an accessible and cost-effective solution for resource-constrained grain farming systems that experience data shortages.